Burndown charts, feature completeness, code quality, pass/fail testing. Dev and test managers have access to lots of data from many sources about an upcoming release. But none of it directly relates to business outcomes. While decisions might be influenced by data, they’re still largely subjective and more often based on experience.

We’re excited to introduce Eggplant Release Insights, a new product designed to gauge the quality of a product or website. Rather than using arbitrary metrics, such as 97 percent of the tests pass, Eggplant Release Insights combines data generated by Eggplant AI with data from across the rest of the CI/CD pipelinetoevaluate release quality and its impact on the user.

By using Eggplant Release Insights for any given product release or update, you’ll get an upfront summary of the status of the release in the form of a rating between zero and five Eggplants.

The rating comprises an aggregated summary of the individual predictors that assess the quality of the release. And the overall release rating gives you an instant view of current status.

So far, we’ve created several predictors inside Eggplant Release Insights, each of which provides specific insights:

Bug Content Predictoranticipates the expected, relative number of undiscovered defects based on the development and test assets. It uses an internal metric of undiscovered defects per thousand lines of codetoassess all aspects of the build process, including the quality of the requirements that define the feature being checked in.

Development Quality Predictoris designed to assess the quality of the whole development process and identify the key risks that impact quality. It helps determine the largest risk factors — such as behaviors like small or largecheck-insoruse of specific languages — present within the currentrelease andassess their impact.

Test CoveragePredictorusescoverage data from regression and exploratory tests to provide an assessment of the level of coverage on the current product.

Performance Predictorhelps analyze the performance of the current release, identify any bottlenecks and place them in the context of the entire release.

Within each of these predictors, you can drill down to explore some of the analysis in more detail. We also support integration into othervisual analysis systems(such as Tableau or Power BI) for custom reporting.

Of course, we plan to expand this list; here’s the next one that will be available soon:

Usability quality predictoranalyzes the UI under development and assesses its usability via a wide range of usabilityheuristics. This provides a user-focused metric of how usable and consistent the current release is to use.

See how Eggplant Release Insights can help you predict the business impacts of your release. If you’re an Eggplant customer,you can downloadit now. If you’re not a customer, you can check it out by requesting anEggplant AI trial license. And, you can always get more info onourwebsite.

We always welcome feedback, so if you have any, please share in the commentssection below.

Peter Cherns joined Eggplant in March 2018 as a product manager for AI and analytics. After originally graduating in physics and earning a PhD in materials science, Peter has worked for more than eight years in the world of enterprise software, both in delivery and product. He also has experience in materials data management, delivering solutions to some of the world's leading aerospace, automotive and consumer electronics organizations.

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About Us

Eggplant provides user-centric, Digital Automation Intelligence solutions that enhance the quality and performance of the digital experience. Only Eggplant enables organizations to test, monitor, analyze, and report on the quality and responsiveness of software applications across different interfaces, platforms, browsers, and devices, including mobile, IoT, desktop, and mainframe.